Overview

Dataset statistics

Number of variables46
Number of observations2129
Missing cells13882
Missing cells (%)14.2%
Duplicate rows20
Duplicate rows (%)0.9%
Total size in memory765.2 KiB
Average record size in memory368.1 B

Variable types

Unsupported3
Categorical23
Text7
Boolean12
DateTime1

Alerts

condition_acces has constant value "Accès libre"Constant
gratuit has constant value "False"Constant
paiement_acte has constant value "True"Constant
paiement_autre has constant value "True"Constant
reservation has constant value "False"Constant
siren_amenageur has constant value "531680445"Constant
contact_amenageur has constant value "assistance.technique@tevgo.fr"Constant
nom_amenageur has constant value "TOTALENERGIES"Constant
nom_operateur has constant value "TOTALENERGIES"Constant
contact_operateur has constant value "assistance.technique@tevgo.fr"Constant
telephone_operateur has constant value "01 85 16 94 02"Constant
nom_enseigne has constant value "Belib'"Constant
tarification has constant value "https://belib.paris"Constant
prise_type_autre has constant value "False"Constant
horaires has constant value "24/7"Constant
Dataset has 20 (0.9%) duplicate rowsDuplicates
accessibilite_pmr is highly overall correlated with date_maj and 6 other fieldsHigh correlation
arrondissement is highly overall correlated with code_insee_commune and 3 other fieldsHigh correlation
code_insee_commune is highly overall correlated with arrondissement and 4 other fieldsHigh correlation
date_maj is highly overall correlated with accessibilite_pmr and 5 other fieldsHigh correlation
id_pdc is highly overall correlated with arrondissement and 4 other fieldsHigh correlation
implantation_station is highly overall correlated with accessibilite_pmr and 7 other fieldsHigh correlation
last_updated is highly overall correlated with arrondissement and 4 other fieldsHigh correlation
nbre_pdc is highly overall correlated with accessibilite_pmr and 11 other fieldsHigh correlation
paiement_cb is highly overall correlated with nbre_pdc and 5 other fieldsHigh correlation
prise_type_2 is highly overall correlated with nbre_pdc and 2 other fieldsHigh correlation
prise_type_3 is highly overall correlated with nbre_pdc and 5 other fieldsHigh correlation
prise_type_chademo is highly overall correlated with nbre_pdc and 5 other fieldsHigh correlation
prise_type_combo_ccs is highly overall correlated with nbre_pdc and 5 other fieldsHigh correlation
prise_type_ef is highly overall correlated with accessibilite_pmr and 7 other fieldsHigh correlation
puissance_nominale is highly overall correlated with implantation_station and 11 other fieldsHigh correlation
raccordement is highly overall correlated with accessibilite_pmr and 8 other fieldsHigh correlation
restriction_gabarit is highly overall correlated with accessibilite_pmr and 7 other fieldsHigh correlation
station_deux_roues is highly overall correlated with puissance_nominale and 1 other fieldsHigh correlation
statut_pdc is highly overall correlated with accessibilite_pmr and 16 other fieldsHigh correlation
url_description_pdc is highly overall correlated with arrondissement and 4 other fieldsHigh correlation
statut_pdc is highly imbalanced (66.7%)Imbalance
implantation_station is highly imbalanced (82.2%)Imbalance
date_maj is highly imbalanced (88.1%)Imbalance
accessibilite_pmr is highly imbalanced (85.5%)Imbalance
restriction_gabarit is highly imbalanced (89.0%)Imbalance
station_deux_roues is highly imbalanced (73.7%)Imbalance
puissance_nominale is highly imbalanced (54.8%)Imbalance
prise_type_ef is highly imbalanced (82.2%)Imbalance
prise_type_2 is highly imbalanced (68.2%)Imbalance
prise_type_combo_ccs is highly imbalanced (56.5%)Imbalance
prise_type_chademo is highly imbalanced (56.5%)Imbalance
prise_type_3 is highly imbalanced (59.5%)Imbalance
raccordement is highly imbalanced (93.4%)Imbalance
id_pdc has 1909 (89.7%) missing valuesMissing
url_description_pdc has 1909 (89.7%) missing valuesMissing
last_updated has 1909 (89.7%) missing valuesMissing
id_pdc_local has 220 (10.3%) missing valuesMissing
id_station_local has 220 (10.3%) missing valuesMissing
id_station_itinerance has 220 (10.3%) missing valuesMissing
nom_station has 220 (10.3%) missing valuesMissing
implantation_station has 220 (10.3%) missing valuesMissing
nbre_pdc has 220 (10.3%) missing valuesMissing
date_maj has 220 (10.3%) missing valuesMissing
condition_acces has 220 (10.3%) missing valuesMissing
gratuit has 220 (10.3%) missing valuesMissing
paiement_acte has 220 (10.3%) missing valuesMissing
paiement_cb has 220 (10.3%) missing valuesMissing
paiement_autre has 220 (10.3%) missing valuesMissing
reservation has 220 (10.3%) missing valuesMissing
observations has 220 (10.3%) missing valuesMissing
siren_amenageur has 220 (10.3%) missing valuesMissing
contact_amenageur has 220 (10.3%) missing valuesMissing
nom_amenageur has 220 (10.3%) missing valuesMissing
nom_operateur has 220 (10.3%) missing valuesMissing
contact_operateur has 220 (10.3%) missing valuesMissing
telephone_operateur has 220 (10.3%) missing valuesMissing
nom_enseigne has 220 (10.3%) missing valuesMissing
tarification has 220 (10.3%) missing valuesMissing
id_pdc_itinerance has 220 (10.3%) missing valuesMissing
date_mise_en_service has 220 (10.3%) missing valuesMissing
accessibilite_pmr has 220 (10.3%) missing valuesMissing
restriction_gabarit has 220 (10.3%) missing valuesMissing
station_deux_roues has 220 (10.3%) missing valuesMissing
puissance_nominale has 220 (10.3%) missing valuesMissing
prise_type_ef has 220 (10.3%) missing valuesMissing
prise_type_2 has 220 (10.3%) missing valuesMissing
prise_type_combo_ccs has 220 (10.3%) missing valuesMissing
prise_type_chademo has 220 (10.3%) missing valuesMissing
prise_type_autre has 220 (10.3%) missing valuesMissing
prise_type_3 has 220 (10.3%) missing valuesMissing
num_pdl has 220 (10.3%) missing valuesMissing
horaires has 220 (10.3%) missing valuesMissing
raccordement has 220 (10.3%) missing valuesMissing
id_pdc is uniformly distributedUniform
url_description_pdc is uniformly distributedUniform
_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
coordonneesxy is an unsupported type, check if it needs cleaning or further analysisUnsupported
observations is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-18 10:35:27.213451
Analysis finished2024-05-18 10:35:44.528752
Duration17.32 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

_id
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size16.8 KiB

id_pdc
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct10
Distinct (%)4.5%
Missing1909
Missing (%)89.7%
Memory size16.8 KiB
FR*V75*EPX20*09*2
22 
FR*V75*EPX11*05*4
22 
FR*V75*EPX20*09*3
22 
FR*V75*E9005*02*1
22 
FR*V75*EPX13*05*1
22 
Other values (5)
110 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters3740
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFR*V75*EPX11*05*4
2nd rowFR*V75*EPX20*09*2
3rd rowFR*V75*EPX20*09*3
4th rowFR*V75*E9005*02*1
5th rowFR*V75*EPX12*04*4

Common Values

ValueCountFrequency (%)
FR*V75*EPX20*09*2 22
 
1.0%
FR*V75*EPX11*05*4 22
 
1.0%
FR*V75*EPX20*09*3 22
 
1.0%
FR*V75*E9005*02*1 22
 
1.0%
FR*V75*EPX13*05*1 22
 
1.0%
FR*V75*EPX12*04*4 22
 
1.0%
FR*V75*EPX18*11*5 22
 
1.0%
FR*V75*EPX15*13*5 22
 
1.0%
FR*V75*EPX15*13*6 22
 
1.0%
FR*V75*EPX19*17*5 22
 
1.0%
(Missing) 1909
89.7%

Length

2024-05-18T12:35:44.854188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:35:45.365700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
fr*v75*epx20*09*2 22
10.0%
fr*v75*epx11*05*4 22
10.0%
fr*v75*epx20*09*3 22
10.0%
fr*v75*e9005*02*1 22
10.0%
fr*v75*epx13*05*1 22
10.0%
fr*v75*epx12*04*4 22
10.0%
fr*v75*epx18*11*5 22
10.0%
fr*v75*epx15*13*5 22
10.0%
fr*v75*epx15*13*6 22
10.0%
fr*v75*epx19*17*5 22
10.0%

Most occurring characters

ValueCountFrequency (%)
* 880
23.5%
5 396
10.6%
1 330
 
8.8%
7 242
 
6.5%
F 220
 
5.9%
V 220
 
5.9%
E 220
 
5.9%
0 220
 
5.9%
R 220
 
5.9%
X 198
 
5.3%
Other values (7) 594
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
* 880
23.5%
5 396
10.6%
1 330
 
8.8%
7 242
 
6.5%
F 220
 
5.9%
V 220
 
5.9%
E 220
 
5.9%
0 220
 
5.9%
R 220
 
5.9%
X 198
 
5.3%
Other values (7) 594
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
* 880
23.5%
5 396
10.6%
1 330
 
8.8%
7 242
 
6.5%
F 220
 
5.9%
V 220
 
5.9%
E 220
 
5.9%
0 220
 
5.9%
R 220
 
5.9%
X 198
 
5.3%
Other values (7) 594
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
* 880
23.5%
5 396
10.6%
1 330
 
8.8%
7 242
 
6.5%
F 220
 
5.9%
V 220
 
5.9%
E 220
 
5.9%
0 220
 
5.9%
R 220
 
5.9%
X 198
 
5.3%
Other values (7) 594
15.9%

statut_pdc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
En service
1909 
Inconnu
198 
Disponible
 
22

Length

Max length10
Median length10
Mean length9.7209958
Min length7

Characters and Unicode

Total characters20696
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDisponible
2nd rowInconnu
3rd rowInconnu
4th rowInconnu
5th rowInconnu

Common Values

ValueCountFrequency (%)
En service 1909
89.7%
Inconnu 198
 
9.3%
Disponible 22
 
1.0%

Length

2024-05-18T12:35:45.962577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:35:46.370535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
en 1909
47.3%
service 1909
47.3%
inconnu 198
 
4.9%
disponible 22
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 3840
18.6%
n 2525
12.2%
c 2107
10.2%
i 1953
9.4%
s 1931
9.3%
1909
9.2%
r 1909
9.2%
E 1909
9.2%
v 1909
9.2%
o 220
 
1.1%
Other values (6) 484
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3840
18.6%
n 2525
12.2%
c 2107
10.2%
i 1953
9.4%
s 1931
9.3%
1909
9.2%
r 1909
9.2%
E 1909
9.2%
v 1909
9.2%
o 220
 
1.1%
Other values (6) 484
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3840
18.6%
n 2525
12.2%
c 2107
10.2%
i 1953
9.4%
s 1931
9.3%
1909
9.2%
r 1909
9.2%
E 1909
9.2%
v 1909
9.2%
o 220
 
1.1%
Other values (6) 484
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3840
18.6%
n 2525
12.2%
c 2107
10.2%
i 1953
9.4%
s 1931
9.3%
1909
9.2%
r 1909
9.2%
E 1909
9.2%
v 1909
9.2%
o 220
 
1.1%
Other values (6) 484
 
2.3%

url_description_pdc
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct10
Distinct (%)4.5%
Missing1909
Missing (%)89.7%
Memory size16.8 KiB
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*2
22 
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX11*05*4
22 
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*3
22 
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*E9005*02*1
22 
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX13*05*1
22 
Other values (5)
110 

Length

Max length315
Median length315
Mean length315
Min length315

Characters and Unicode

Total characters69300
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX11*05*4
2nd rowhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*2
3rd rowhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*3
4th rowhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*E9005*02*1
5th rowhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX12*04*4

Common Values

ValueCountFrequency (%)
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*2 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX11*05*4 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*3 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*E9005*02*1 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX13*05*1 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX12*04*4 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX18*11*5 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX15*13*5 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX15*13*6 22
 
1.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX19*17*5 22
 
1.0%
(Missing) 1909
89.7%

Length

2024-05-18T12:35:46.803697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:35:47.434914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx20*09*2 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx11*05*4 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx20*09*3 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*e9005*02*1 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx13*05*1 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx12*04*4 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx18*11*5 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx15*13*5 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx15*13*6 22
10.0%
https://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=fr*v75*epx19*17*5 22
10.0%

Most occurring characters

ValueCountFrequency (%)
e 7480
 
10.8%
s 5720
 
8.3%
i 5720
 
8.3%
t 4840
 
7.0%
c 3960
 
5.7%
a 3740
 
5.4%
d 3080
 
4.4%
r 2640
 
3.8%
n 2640
 
3.8%
u 2640
 
3.8%
Other values (38) 26840
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7480
 
10.8%
s 5720
 
8.3%
i 5720
 
8.3%
t 4840
 
7.0%
c 3960
 
5.7%
a 3740
 
5.4%
d 3080
 
4.4%
r 2640
 
3.8%
n 2640
 
3.8%
u 2640
 
3.8%
Other values (38) 26840
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7480
 
10.8%
s 5720
 
8.3%
i 5720
 
8.3%
t 4840
 
7.0%
c 3960
 
5.7%
a 3740
 
5.4%
d 3080
 
4.4%
r 2640
 
3.8%
n 2640
 
3.8%
u 2640
 
3.8%
Other values (38) 26840
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7480
 
10.8%
s 5720
 
8.3%
i 5720
 
8.3%
t 4840
 
7.0%
c 3960
 
5.7%
a 3740
 
5.4%
d 3080
 
4.4%
r 2640
 
3.8%
n 2640
 
3.8%
u 2640
 
3.8%
Other values (38) 26840
38.7%

last_updated
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)3.6%
Missing1909
Missing (%)89.7%
Memory size16.8 KiB
2024-01-03T03:30:06+00:00
44 
2022-09-23T02:30:02+00:00
44 
2022-06-13T02:30:01+00:00
22 
2022-11-22T03:20:17+00:00
22 
2023-06-06T02:30:04+00:00
22 
Other values (3)
66 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters5500
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-13T02:30:01+00:00
2nd row2022-09-23T02:30:02+00:00
3rd row2022-09-23T02:30:02+00:00
4th row2022-11-22T03:20:17+00:00
5th row2023-05-24T02:30:05+00:00

Common Values

ValueCountFrequency (%)
2024-01-03T03:30:06+00:00 44
 
2.1%
2022-09-23T02:30:02+00:00 44
 
2.1%
2022-06-13T02:30:01+00:00 22
 
1.0%
2022-11-22T03:20:17+00:00 22
 
1.0%
2023-06-06T02:30:04+00:00 22
 
1.0%
2023-05-24T02:30:05+00:00 22
 
1.0%
2023-10-19T02:30:06+00:00 22
 
1.0%
2024-02-26T03:30:05+00:00 22
 
1.0%
(Missing) 1909
89.7%

Length

2024-05-18T12:35:48.846428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:35:49.325150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-03t03:30:06+00:00 44
20.0%
2022-09-23t02:30:02+00:00 44
20.0%
2022-06-13t02:30:01+00:00 22
10.0%
2022-11-22t03:20:17+00:00 22
10.0%
2023-06-06t02:30:04+00:00 22
10.0%
2023-05-24t02:30:05+00:00 22
10.0%
2023-10-19t02:30:06+00:00 22
10.0%
2024-02-26t03:30:05+00:00 22
10.0%

Most occurring characters

ValueCountFrequency (%)
0 2002
36.4%
2 880
16.0%
: 660
 
12.0%
3 462
 
8.4%
- 440
 
8.0%
T 220
 
4.0%
+ 220
 
4.0%
1 198
 
3.6%
6 154
 
2.8%
4 110
 
2.0%
Other values (3) 154
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2002
36.4%
2 880
16.0%
: 660
 
12.0%
3 462
 
8.4%
- 440
 
8.0%
T 220
 
4.0%
+ 220
 
4.0%
1 198
 
3.6%
6 154
 
2.8%
4 110
 
2.0%
Other values (3) 154
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2002
36.4%
2 880
16.0%
: 660
 
12.0%
3 462
 
8.4%
- 440
 
8.0%
T 220
 
4.0%
+ 220
 
4.0%
1 198
 
3.6%
6 154
 
2.8%
4 110
 
2.0%
Other values (3) 154
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2002
36.4%
2 880
16.0%
: 660
 
12.0%
3 462
 
8.4%
- 440
 
8.0%
T 220
 
4.0%
+ 220
 
4.0%
1 198
 
3.6%
6 154
 
2.8%
4 110
 
2.0%
Other values (3) 154
 
2.8%

coordonneesxy
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size16.8 KiB
Distinct377
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
2024-05-18T12:35:50.559362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length49
Median length42
Mean length31.273368
Min length23

Characters and Unicode

Total characters66581
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8 Boulevard Voltaire 75011 Paris
2nd row6 Rue Guébriant 75020 Paris
3rd row6 Rue Guébriant 75020 Paris
4th row55 Rue Monge 75005 Paris
5th row251 Rue de Charenton 75012 Paris
ValueCountFrequency (%)
paris 2129
 
17.3%
rue 1420
 
11.5%
de 584
 
4.7%
avenue 387
 
3.1%
75015 258
 
2.1%
boulevard 229
 
1.9%
75016 213
 
1.7%
du 165
 
1.3%
75020 158
 
1.3%
75013 147
 
1.2%
Other values (532) 6628
53.8%
2024-05-18T12:35:52.285162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10189
15.3%
e 5435
 
8.2%
a 4209
 
6.3%
r 4174
 
6.3%
i 3651
 
5.5%
0 3174
 
4.8%
u 3102
 
4.7%
s 3000
 
4.5%
5 2903
 
4.4%
7 2599
 
3.9%
Other values (61) 24145
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10189
15.3%
e 5435
 
8.2%
a 4209
 
6.3%
r 4174
 
6.3%
i 3651
 
5.5%
0 3174
 
4.8%
u 3102
 
4.7%
s 3000
 
4.5%
5 2903
 
4.4%
7 2599
 
3.9%
Other values (61) 24145
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10189
15.3%
e 5435
 
8.2%
a 4209
 
6.3%
r 4174
 
6.3%
i 3651
 
5.5%
0 3174
 
4.8%
u 3102
 
4.7%
s 3000
 
4.5%
5 2903
 
4.4%
7 2599
 
3.9%
Other values (61) 24145
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10189
15.3%
e 5435
 
8.2%
a 4209
 
6.3%
r 4174
 
6.3%
i 3651
 
5.5%
0 3174
 
4.8%
u 3102
 
4.7%
s 3000
 
4.5%
5 2903
 
4.4%
7 2599
 
3.9%
Other values (61) 24145
36.3%

code_insee_commune
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.9%
Missing15
Missing (%)0.7%
Memory size16.8 KiB
75115
258 
75116
213 
75120
158 
75113
147 
75112
134 
Other values (15)
1204 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10570
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row75111
2nd row75120
3rd row75120
4th row75105
5th row75112

Common Values

ValueCountFrequency (%)
75115 258
12.1%
75116 213
 
10.0%
75120 158
 
7.4%
75113 147
 
6.9%
75112 134
 
6.3%
75117 132
 
6.2%
75119 123
 
5.8%
75118 119
 
5.6%
75111 109
 
5.1%
75107 107
 
5.0%
Other values (10) 614
28.8%

Length

2024-05-18T12:35:52.800618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
75115 258
12.2%
75116 213
 
10.1%
75120 158
 
7.5%
75113 147
 
7.0%
75112 134
 
6.3%
75117 132
 
6.2%
75119 123
 
5.8%
75118 119
 
5.6%
75111 109
 
5.2%
75107 107
 
5.1%
Other values (10) 614
29.0%

Most occurring characters

ValueCountFrequency (%)
1 3634
34.4%
5 2459
23.3%
7 2353
22.3%
0 791
 
7.5%
2 333
 
3.2%
6 280
 
2.6%
8 220
 
2.1%
9 179
 
1.7%
3 173
 
1.6%
4 148
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3634
34.4%
5 2459
23.3%
7 2353
22.3%
0 791
 
7.5%
2 333
 
3.2%
6 280
 
2.6%
8 220
 
2.1%
9 179
 
1.7%
3 173
 
1.6%
4 148
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3634
34.4%
5 2459
23.3%
7 2353
22.3%
0 791
 
7.5%
2 333
 
3.2%
6 280
 
2.6%
8 220
 
2.1%
9 179
 
1.7%
3 173
 
1.6%
4 148
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3634
34.4%
5 2459
23.3%
7 2353
22.3%
0 791
 
7.5%
2 333
 
3.2%
6 280
 
2.6%
8 220
 
2.1%
9 179
 
1.7%
3 173
 
1.6%
4 148
 
1.4%

arrondissement
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
15e Arrondissement
258 
16e Arrondissement
213 
20e Arrondissement
155 
Paris centre
154 
13e Arrondissement
147 
Other values (12)
1202 

Length

Max length18
Median length18
Mean length17.565993
Min length12

Characters and Unicode

Total characters37398
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11e Arrondissement
2nd row20e Arrondissement
3rd row20e Arrondissement
4th row05e Arrondissement
5th row12e Arrondissement

Common Values

ValueCountFrequency (%)
15e Arrondissement 258
12.1%
16e Arrondissement 213
 
10.0%
20e Arrondissement 155
 
7.3%
Paris centre 154
 
7.2%
13e Arrondissement 147
 
6.9%
12e Arrondissement 128
 
6.0%
17e Arrondissement 127
 
6.0%
19e Arrondissement 126
 
5.9%
18e Arrondissement 124
 
5.8%
08e Arrondissement 116
 
5.4%
Other values (7) 581
27.3%

Length

2024-05-18T12:35:53.239326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arrondissement 1975
46.4%
15e 258
 
6.1%
16e 213
 
5.0%
20e 155
 
3.6%
paris 154
 
3.6%
centre 154
 
3.6%
13e 147
 
3.5%
12e 128
 
3.0%
17e 127
 
3.0%
19e 126
 
3.0%
Other values (9) 821
19.3%

Most occurring characters

ValueCountFrequency (%)
e 6233
16.7%
r 4258
11.4%
n 4104
11.0%
s 4104
11.0%
i 2129
 
5.7%
t 2129
 
5.7%
2129
 
5.7%
A 1975
 
5.3%
m 1975
 
5.3%
d 1975
 
5.3%
Other values (14) 6387
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6233
16.7%
r 4258
11.4%
n 4104
11.0%
s 4104
11.0%
i 2129
 
5.7%
t 2129
 
5.7%
2129
 
5.7%
A 1975
 
5.3%
m 1975
 
5.3%
d 1975
 
5.3%
Other values (14) 6387
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6233
16.7%
r 4258
11.4%
n 4104
11.0%
s 4104
11.0%
i 2129
 
5.7%
t 2129
 
5.7%
2129
 
5.7%
A 1975
 
5.3%
m 1975
 
5.3%
d 1975
 
5.3%
Other values (14) 6387
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6233
16.7%
r 4258
11.4%
n 4104
11.0%
s 4104
11.0%
i 2129
 
5.7%
t 2129
 
5.7%
2129
 
5.7%
A 1975
 
5.3%
m 1975
 
5.3%
d 1975
 
5.3%
Other values (14) 6387
17.1%

id_pdc_local
Text

MISSING 

Distinct1889
Distinct (%)99.0%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2024-05-18T12:35:53.879613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length17
Mean length17.135149
Min length17

Characters and Unicode

Total characters32711
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1879 ?
Unique (%)98.4%

Sample

1st rowFR*V75*E9007*02*3
2nd rowFR*V75*E9003*01*2
3rd rowFR*V75*E9006*04*2
4th rowFR*V75*E9013*05*1
5th rowFR*V75*E9015*04*3
ValueCountFrequency (%)
fr*v75*e9017*06*1 3
 
0.2%
fr*v75*e9007*02*3 3
 
0.2%
fr*v75*e9003*01*2 3
 
0.2%
fr*v75*e9006*04*2 3
 
0.2%
fr*v75*e9013*05*1 3
 
0.2%
fr*v75*e9015*04*3 3
 
0.2%
fr*v75*e9016*03*2 3
 
0.2%
fr*v75*e9016*03*3 3
 
0.2%
fr*v75*e9017*05*1 3
 
0.2%
fr*v75*e9018*03*3 3
 
0.2%
Other values (1879) 1879
98.4%
2024-05-18T12:35:55.003967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 7687
23.5%
5 2620
 
8.0%
1 2589
 
7.9%
7 2363
 
7.2%
0 2153
 
6.6%
E 1924
 
5.9%
R 1909
 
5.8%
V 1909
 
5.8%
F 1909
 
5.8%
P 1643
 
5.0%
Other values (17) 6005
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
* 7687
23.5%
5 2620
 
8.0%
1 2589
 
7.9%
7 2363
 
7.2%
0 2153
 
6.6%
E 1924
 
5.9%
R 1909
 
5.8%
V 1909
 
5.8%
F 1909
 
5.8%
P 1643
 
5.0%
Other values (17) 6005
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
* 7687
23.5%
5 2620
 
8.0%
1 2589
 
7.9%
7 2363
 
7.2%
0 2153
 
6.6%
E 1924
 
5.9%
R 1909
 
5.8%
V 1909
 
5.8%
F 1909
 
5.8%
P 1643
 
5.0%
Other values (17) 6005
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
* 7687
23.5%
5 2620
 
8.0%
1 2589
 
7.9%
7 2363
 
7.2%
0 2153
 
6.6%
E 1924
 
5.9%
R 1909
 
5.8%
V 1909
 
5.8%
F 1909
 
5.8%
P 1643
 
5.0%
Other values (17) 6005
18.4%

id_station_local
Text

MISSING 

Distinct371
Distinct (%)19.4%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2024-05-18T12:35:55.857531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length15.053431
Min length15

Characters and Unicode

Total characters28737
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFR*V75*P9007*02
2nd rowFR*V75*P9003*01
3rd rowFR*V75*P9006*04
4th rowFR*V75*P9013*05
5th rowFR*V75*P9015*04
ValueCountFrequency (%)
fr*v75*phbsag*lob 21
 
1.1%
fr*v75*phbsai*aug 15
 
0.8%
fr*v75*phbsae*pda 15
 
0.8%
fr*v75*ppx12*14 7
 
0.4%
fr*v75*ppx12*15 7
 
0.4%
fr*v75*ppx17*16 7
 
0.4%
fr*v75*ppx19*02 7
 
0.4%
fr*v75*ppx18*06 7
 
0.4%
fr*v75*ppx12*18 7
 
0.4%
fr*v75*ppx07*05 7
 
0.4%
Other values (361) 1809
94.8%
2024-05-18T12:35:57.248636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 5727
19.9%
P 3552
12.4%
5 2376
8.3%
7 2321
8.1%
1 2191
 
7.6%
0 2099
 
7.3%
F 1909
 
6.6%
R 1909
 
6.6%
V 1909
 
6.6%
X 1628
 
5.7%
Other values (17) 3116
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
* 5727
19.9%
P 3552
12.4%
5 2376
8.3%
7 2321
8.1%
1 2191
 
7.6%
0 2099
 
7.3%
F 1909
 
6.6%
R 1909
 
6.6%
V 1909
 
6.6%
X 1628
 
5.7%
Other values (17) 3116
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
* 5727
19.9%
P 3552
12.4%
5 2376
8.3%
7 2321
8.1%
1 2191
 
7.6%
0 2099
 
7.3%
F 1909
 
6.6%
R 1909
 
6.6%
V 1909
 
6.6%
X 1628
 
5.7%
Other values (17) 3116
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
* 5727
19.9%
P 3552
12.4%
5 2376
8.3%
7 2321
8.1%
1 2191
 
7.6%
0 2099
 
7.3%
F 1909
 
6.6%
R 1909
 
6.6%
V 1909
 
6.6%
X 1628
 
5.7%
Other values (17) 3116
10.8%

id_station_itinerance
Text

MISSING 

Distinct371
Distinct (%)19.4%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2024-05-18T12:35:58.135891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.053431
Min length12

Characters and Unicode

Total characters23010
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFRV75P900702
2nd rowFRV75P900301
3rd rowFRV75P900604
4th rowFRV75P901305
5th rowFRV75P901504
ValueCountFrequency (%)
frv75phbsaglob 21
 
1.1%
frv75phbsaiaug 15
 
0.8%
frv75phbsaepda 15
 
0.8%
frv75ppx1214 7
 
0.4%
frv75ppx1215 7
 
0.4%
frv75ppx1716 7
 
0.4%
frv75ppx1902 7
 
0.4%
frv75ppx1806 7
 
0.4%
frv75ppx1218 7
 
0.4%
frv75ppx0705 7
 
0.4%
Other values (361) 1809
94.8%
2024-05-18T12:35:59.506639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 3552
15.4%
5 2376
10.3%
7 2321
10.1%
1 2191
9.5%
0 2099
9.1%
F 1909
8.3%
R 1909
8.3%
V 1909
8.3%
X 1628
7.1%
2 679
 
3.0%
Other values (16) 2437
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 3552
15.4%
5 2376
10.3%
7 2321
10.1%
1 2191
9.5%
0 2099
9.1%
F 1909
8.3%
R 1909
8.3%
V 1909
8.3%
X 1628
7.1%
2 679
 
3.0%
Other values (16) 2437
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 3552
15.4%
5 2376
10.3%
7 2321
10.1%
1 2191
9.5%
0 2099
9.1%
F 1909
8.3%
R 1909
8.3%
V 1909
8.3%
X 1628
7.1%
2 679
 
3.0%
Other values (16) 2437
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 3552
15.4%
5 2376
10.3%
7 2321
10.1%
1 2191
9.5%
0 2099
9.1%
F 1909
8.3%
R 1909
8.3%
V 1909
8.3%
X 1628
7.1%
2 679
 
3.0%
Other values (16) 2437
10.6%

nom_station
Text

MISSING 

Distinct371
Distinct (%)19.4%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2024-05-18T12:36:00.745329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length45
Median length38
Mean length27.942378
Min length19

Characters and Unicode

Total characters53342
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowParis | Avenue de Saxe 10
2nd rowParis | Rue Réaumur 5
3rd rowParis | Rue de Sevres 20
4th rowParis | Rue de Tolbiac 145
5th rowParis | Rue de la Convention 99
ValueCountFrequency (%)
paris 1909
17.1%
1909
17.1%
rue 1238
 
11.1%
de 519
 
4.7%
avenue 343
 
3.1%
boulevard 207
 
1.9%
du 150
 
1.3%
2 146
 
1.3%
la 135
 
1.2%
1 123
 
1.1%
Other values (505) 4473
40.1%
2024-05-18T12:36:02.472970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9430
17.7%
e 4973
 
9.3%
a 3834
 
7.2%
r 3828
 
7.2%
i 3368
 
6.3%
u 2861
 
5.4%
s 2771
 
5.2%
P 2271
 
4.3%
| 1909
 
3.6%
n 1614
 
3.0%
Other values (61) 16483
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9430
17.7%
e 4973
 
9.3%
a 3834
 
7.2%
r 3828
 
7.2%
i 3368
 
6.3%
u 2861
 
5.4%
s 2771
 
5.2%
P 2271
 
4.3%
| 1909
 
3.6%
n 1614
 
3.0%
Other values (61) 16483
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9430
17.7%
e 4973
 
9.3%
a 3834
 
7.2%
r 3828
 
7.2%
i 3368
 
6.3%
u 2861
 
5.4%
s 2771
 
5.2%
P 2271
 
4.3%
| 1909
 
3.6%
n 1614
 
3.0%
Other values (61) 16483
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9430
17.7%
e 4973
 
9.3%
a 3834
 
7.2%
r 3828
 
7.2%
i 3368
 
6.3%
u 2861
 
5.4%
s 2771
 
5.2%
P 2271
 
4.3%
| 1909
 
3.6%
n 1614
 
3.0%
Other values (61) 16483
30.9%

implantation_station
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
Voirie
1858 
Parking public
 
51

Length

Max length14
Median length6
Mean length6.2137245
Min length6

Characters and Unicode

Total characters11862
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVoirie
2nd rowVoirie
3rd rowVoirie
4th rowVoirie
5th rowVoirie

Common Values

ValueCountFrequency (%)
Voirie 1858
87.3%
Parking public 51
 
2.4%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:03.033348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:03.442723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
voirie 1858
94.8%
parking 51
 
2.6%
public 51
 
2.6%

Most occurring characters

ValueCountFrequency (%)
i 3818
32.2%
r 1909
16.1%
o 1858
15.7%
V 1858
15.7%
e 1858
15.7%
P 51
 
0.4%
a 51
 
0.4%
k 51
 
0.4%
n 51
 
0.4%
g 51
 
0.4%
Other values (6) 306
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3818
32.2%
r 1909
16.1%
o 1858
15.7%
V 1858
15.7%
e 1858
15.7%
P 51
 
0.4%
a 51
 
0.4%
k 51
 
0.4%
n 51
 
0.4%
g 51
 
0.4%
Other values (6) 306
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3818
32.2%
r 1909
16.1%
o 1858
15.7%
V 1858
15.7%
e 1858
15.7%
P 51
 
0.4%
a 51
 
0.4%
k 51
 
0.4%
n 51
 
0.4%
g 51
 
0.4%
Other values (6) 306
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3818
32.2%
r 1909
16.1%
o 1858
15.7%
V 1858
15.7%
e 1858
15.7%
P 51
 
0.4%
a 51
 
0.4%
k 51
 
0.4%
n 51
 
0.4%
g 51
 
0.4%
Other values (6) 306
 
2.6%

nbre_pdc
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.4%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
6
792 
5
340 
7
259 
3
242 
4
224 
Other values (3)
 
52

Length

Max length2
Median length1
Mean length1.0267156
Min length1

Characters and Unicode

Total characters1960
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
6 792
37.2%
5 340
16.0%
7 259
 
12.2%
3 242
 
11.4%
4 224
 
10.5%
15 30
 
1.4%
21 21
 
1.0%
1 1
 
< 0.1%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:03.845651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:04.307524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
6 792
41.5%
5 340
17.8%
7 259
 
13.6%
3 242
 
12.7%
4 224
 
11.7%
15 30
 
1.6%
21 21
 
1.1%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
6 792
40.4%
5 370
18.9%
7 259
 
13.2%
3 242
 
12.3%
4 224
 
11.4%
1 52
 
2.7%
2 21
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 792
40.4%
5 370
18.9%
7 259
 
13.2%
3 242
 
12.3%
4 224
 
11.4%
1 52
 
2.7%
2 21
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 792
40.4%
5 370
18.9%
7 259
 
13.2%
3 242
 
12.3%
4 224
 
11.4%
1 52
 
2.7%
2 21
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 792
40.4%
5 370
18.9%
7 259
 
13.2%
3 242
 
12.3%
4 224
 
11.4%
1 52
 
2.7%
2 21
 
1.1%

date_maj
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct13
Distinct (%)0.7%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2023-04-03
1818 
2023-11-15
 
15
2023-12-11
 
15
2023-06-21
 
10
2023-04-12
 
10
Other values (8)
 
41

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters19090
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04-03
2nd row2023-04-03
3rd row2023-04-03
4th row2023-04-03
5th row2023-04-03

Common Values

ValueCountFrequency (%)
2023-04-03 1818
85.4%
2023-11-15 15
 
0.7%
2023-12-11 15
 
0.7%
2023-06-21 10
 
0.5%
2023-04-12 10
 
0.5%
2024-01-19 7
 
0.3%
2024-02-07 6
 
0.3%
2023-09-05 5
 
0.2%
2024-03-18 5
 
0.2%
2023-12-13 5
 
0.2%
Other values (3) 13
 
0.6%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:04.778711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-04-03 1818
95.2%
2023-11-15 15
 
0.8%
2023-12-11 15
 
0.8%
2023-06-21 10
 
0.5%
2023-04-12 10
 
0.5%
2024-01-19 7
 
0.4%
2024-02-07 6
 
0.3%
2023-09-05 5
 
0.3%
2024-03-18 5
 
0.3%
2023-12-13 5
 
0.3%
Other values (3) 13
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 5620
29.4%
2 3872
20.3%
- 3818
20.0%
3 3719
19.5%
4 1851
 
9.7%
1 147
 
0.8%
5 25
 
0.1%
9 17
 
0.1%
6 10
 
0.1%
7 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5620
29.4%
2 3872
20.3%
- 3818
20.0%
3 3719
19.5%
4 1851
 
9.7%
1 147
 
0.8%
5 25
 
0.1%
9 17
 
0.1%
6 10
 
0.1%
7 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5620
29.4%
2 3872
20.3%
- 3818
20.0%
3 3719
19.5%
4 1851
 
9.7%
1 147
 
0.8%
5 25
 
0.1%
9 17
 
0.1%
6 10
 
0.1%
7 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5620
29.4%
2 3872
20.3%
- 3818
20.0%
3 3719
19.5%
4 1851
 
9.7%
1 147
 
0.8%
5 25
 
0.1%
9 17
 
0.1%
6 10
 
0.1%
7 6
 
< 0.1%

condition_acces
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
Accès libre
1909 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters20999
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccès libre
2nd rowAccès libre
3rd rowAccès libre
4th rowAccès libre
5th rowAccès libre

Common Values

ValueCountFrequency (%)
Accès libre 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:05.184595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:05.522330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
accès 1909
50.0%
libre 1909
50.0%

Most occurring characters

ValueCountFrequency (%)
c 3818
18.2%
A 1909
9.1%
è 1909
9.1%
s 1909
9.1%
1909
9.1%
l 1909
9.1%
i 1909
9.1%
b 1909
9.1%
r 1909
9.1%
e 1909
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 3818
18.2%
A 1909
9.1%
è 1909
9.1%
s 1909
9.1%
1909
9.1%
l 1909
9.1%
i 1909
9.1%
b 1909
9.1%
r 1909
9.1%
e 1909
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 3818
18.2%
A 1909
9.1%
è 1909
9.1%
s 1909
9.1%
1909
9.1%
l 1909
9.1%
i 1909
9.1%
b 1909
9.1%
r 1909
9.1%
e 1909
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 3818
18.2%
A 1909
9.1%
è 1909
9.1%
s 1909
9.1%
1909
9.1%
l 1909
9.1%
i 1909
9.1%
b 1909
9.1%
r 1909
9.1%
e 1909
9.1%

gratuit
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1909 
(Missing)
220 
ValueCountFrequency (%)
False 1909
89.7%
(Missing) 220
 
10.3%
2024-05-18T12:36:05.827676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

paiement_acte
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
True
1909 
(Missing)
220 
ValueCountFrequency (%)
True 1909
89.7%
(Missing) 220
 
10.3%
2024-05-18T12:36:06.139948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

paiement_cb
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
True
1677 
False
232 
(Missing)
220 
ValueCountFrequency (%)
True 1677
78.8%
False 232
 
10.9%
(Missing) 220
 
10.3%
2024-05-18T12:36:06.610132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

paiement_autre
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
True
1909 
(Missing)
220 
ValueCountFrequency (%)
True 1909
89.7%
(Missing) 220
 
10.3%
2024-05-18T12:36:06.938931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

reservation
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1909 
(Missing)
220 
ValueCountFrequency (%)
False 1909
89.7%
(Missing) 220
 
10.3%
2024-05-18T12:36:07.248250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

observations
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing220
Missing (%)10.3%
Memory size16.8 KiB

siren_amenageur
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
531680445
1909 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters17181
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row531680445
2nd row531680445
3rd row531680445
4th row531680445
5th row531680445

Common Values

ValueCountFrequency (%)
531680445 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:07.949537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:08.278118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
531680445 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
5 3818
22.2%
4 3818
22.2%
3 1909
11.1%
1 1909
11.1%
6 1909
11.1%
8 1909
11.1%
0 1909
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 3818
22.2%
4 3818
22.2%
3 1909
11.1%
1 1909
11.1%
6 1909
11.1%
8 1909
11.1%
0 1909
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 3818
22.2%
4 3818
22.2%
3 1909
11.1%
1 1909
11.1%
6 1909
11.1%
8 1909
11.1%
0 1909
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 3818
22.2%
4 3818
22.2%
3 1909
11.1%
1 1909
11.1%
6 1909
11.1%
8 1909
11.1%
0 1909
11.1%

contact_amenageur
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
assistance.technique@tevgo.fr
1909 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters55361
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowassistance.technique@tevgo.fr
2nd rowassistance.technique@tevgo.fr
3rd rowassistance.technique@tevgo.fr
4th rowassistance.technique@tevgo.fr
5th rowassistance.technique@tevgo.fr

Common Values

ValueCountFrequency (%)
assistance.technique@tevgo.fr 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:08.718647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:09.057430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
assistance.technique@tevgo.fr 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

nom_amenageur
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
TOTALENERGIES
1909 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters24817
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTALENERGIES
2nd rowTOTALENERGIES
3rd rowTOTALENERGIES
4th rowTOTALENERGIES
5th rowTOTALENERGIES

Common Values

ValueCountFrequency (%)
TOTALENERGIES 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:09.608278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:10.422845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
totalenergies 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

nom_operateur
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
TOTALENERGIES
1909 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters24817
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTALENERGIES
2nd rowTOTALENERGIES
3rd rowTOTALENERGIES
4th rowTOTALENERGIES
5th rowTOTALENERGIES

Common Values

ValueCountFrequency (%)
TOTALENERGIES 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:10.867090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:11.281346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
totalenergies 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 5727
23.1%
T 3818
15.4%
O 1909
 
7.7%
A 1909
 
7.7%
L 1909
 
7.7%
N 1909
 
7.7%
R 1909
 
7.7%
G 1909
 
7.7%
I 1909
 
7.7%
S 1909
 
7.7%

contact_operateur
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
assistance.technique@tevgo.fr
1909 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters55361
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowassistance.technique@tevgo.fr
2nd rowassistance.technique@tevgo.fr
3rd rowassistance.technique@tevgo.fr
4th rowassistance.technique@tevgo.fr
5th rowassistance.technique@tevgo.fr

Common Values

ValueCountFrequency (%)
assistance.technique@tevgo.fr 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:11.666504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:12.072580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
assistance.technique@tevgo.fr 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7636
13.8%
t 5727
10.3%
s 5727
10.3%
a 3818
 
6.9%
i 3818
 
6.9%
n 3818
 
6.9%
c 3818
 
6.9%
. 3818
 
6.9%
h 1909
 
3.4%
q 1909
 
3.4%
Other values (7) 13363
24.1%

telephone_operateur
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
01 85 16 94 02
1909 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters26726
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01 85 16 94 02
2nd row01 85 16 94 02
3rd row01 85 16 94 02
4th row01 85 16 94 02
5th row01 85 16 94 02

Common Values

ValueCountFrequency (%)
01 85 16 94 02 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:12.480826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:12.858784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
01 1909
20.0%
85 1909
20.0%
16 1909
20.0%
94 1909
20.0%
02 1909
20.0%

Most occurring characters

ValueCountFrequency (%)
7636
28.6%
0 3818
14.3%
1 3818
14.3%
8 1909
 
7.1%
5 1909
 
7.1%
6 1909
 
7.1%
9 1909
 
7.1%
4 1909
 
7.1%
2 1909
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7636
28.6%
0 3818
14.3%
1 3818
14.3%
8 1909
 
7.1%
5 1909
 
7.1%
6 1909
 
7.1%
9 1909
 
7.1%
4 1909
 
7.1%
2 1909
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7636
28.6%
0 3818
14.3%
1 3818
14.3%
8 1909
 
7.1%
5 1909
 
7.1%
6 1909
 
7.1%
9 1909
 
7.1%
4 1909
 
7.1%
2 1909
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7636
28.6%
0 3818
14.3%
1 3818
14.3%
8 1909
 
7.1%
5 1909
 
7.1%
6 1909
 
7.1%
9 1909
 
7.1%
4 1909
 
7.1%
2 1909
 
7.1%

nom_enseigne
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
Belib'
1909 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters11454
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBelib'
2nd rowBelib'
3rd rowBelib'
4th rowBelib'
5th rowBelib'

Common Values

ValueCountFrequency (%)
Belib' 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:13.249148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:13.602438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
belib 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
B 1909
16.7%
e 1909
16.7%
l 1909
16.7%
i 1909
16.7%
b 1909
16.7%
' 1909
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1909
16.7%
e 1909
16.7%
l 1909
16.7%
i 1909
16.7%
b 1909
16.7%
' 1909
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1909
16.7%
e 1909
16.7%
l 1909
16.7%
i 1909
16.7%
b 1909
16.7%
' 1909
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1909
16.7%
e 1909
16.7%
l 1909
16.7%
i 1909
16.7%
b 1909
16.7%
' 1909
16.7%

tarification
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
https://belib.paris
1909 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters36271
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://belib.paris
2nd rowhttps://belib.paris
3rd rowhttps://belib.paris
4th rowhttps://belib.paris
5th rowhttps://belib.paris

Common Values

ValueCountFrequency (%)
https://belib.paris 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:13.983686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:14.346133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
https://belib.paris 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
t 3818
10.5%
s 3818
10.5%
p 3818
10.5%
b 3818
10.5%
/ 3818
10.5%
i 3818
10.5%
h 1909
 
5.3%
: 1909
 
5.3%
e 1909
 
5.3%
l 1909
 
5.3%
Other values (3) 5727
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3818
10.5%
s 3818
10.5%
p 3818
10.5%
b 3818
10.5%
/ 3818
10.5%
i 3818
10.5%
h 1909
 
5.3%
: 1909
 
5.3%
e 1909
 
5.3%
l 1909
 
5.3%
Other values (3) 5727
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3818
10.5%
s 3818
10.5%
p 3818
10.5%
b 3818
10.5%
/ 3818
10.5%
i 3818
10.5%
h 1909
 
5.3%
: 1909
 
5.3%
e 1909
 
5.3%
l 1909
 
5.3%
Other values (3) 5727
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3818
10.5%
s 3818
10.5%
p 3818
10.5%
b 3818
10.5%
/ 3818
10.5%
i 3818
10.5%
h 1909
 
5.3%
: 1909
 
5.3%
e 1909
 
5.3%
l 1909
 
5.3%
Other values (3) 5727
15.8%

id_pdc_itinerance
Text

MISSING 

Distinct1889
Distinct (%)99.0%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2024-05-18T12:36:14.980852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.108434
Min length13

Characters and Unicode

Total characters25024
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1879 ?
Unique (%)98.4%

Sample

1st rowFRV75E9007023
2nd rowFRV75E9003012
3rd rowFRV75E9006042
4th rowFRV75E9013051
5th rowFRV75E9015043
ValueCountFrequency (%)
frv75e9017061 3
 
0.2%
frv75e9007023 3
 
0.2%
frv75e9003012 3
 
0.2%
frv75e9006042 3
 
0.2%
frv75e9013051 3
 
0.2%
frv75e9015043 3
 
0.2%
frv75e9016032 3
 
0.2%
frv75e9016033 3
 
0.2%
frv75e9017051 3
 
0.2%
frv75e9018033 3
 
0.2%
Other values (1879) 1879
98.4%
2024-05-18T12:36:16.262250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2620
10.5%
1 2589
10.3%
7 2363
9.4%
0 2153
8.6%
E 1924
7.7%
R 1909
7.6%
V 1909
7.6%
F 1909
7.6%
P 1643
 
6.6%
X 1628
 
6.5%
Other values (16) 4377
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 2620
10.5%
1 2589
10.3%
7 2363
9.4%
0 2153
8.6%
E 1924
7.7%
R 1909
7.6%
V 1909
7.6%
F 1909
7.6%
P 1643
 
6.6%
X 1628
 
6.5%
Other values (16) 4377
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 2620
10.5%
1 2589
10.3%
7 2363
9.4%
0 2153
8.6%
E 1924
7.7%
R 1909
7.6%
V 1909
7.6%
F 1909
7.6%
P 1643
 
6.6%
X 1628
 
6.5%
Other values (16) 4377
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 2620
10.5%
1 2589
10.3%
7 2363
9.4%
0 2153
8.6%
E 1924
7.7%
R 1909
7.6%
V 1909
7.6%
F 1909
7.6%
P 1643
 
6.6%
X 1628
 
6.5%
Other values (16) 4377
17.5%

date_mise_en_service
Date

MISSING 

Distinct88
Distinct (%)4.6%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
Minimum2021-03-01 00:00:00
Maximum2024-03-19 00:00:00
2024-05-18T12:36:16.824296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-18T12:36:17.381835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

accessibilite_pmr
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.2%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
Non accessible
1835 
Réservé PMR
 
44
Accessible mais non réservé PMR
 
15
Accessibilité inconnue
 
15

Length

Max length31
Median length14
Mean length14.127292
Min length11

Characters and Unicode

Total characters26969
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon accessible
2nd rowNon accessible
3rd rowNon accessible
4th rowNon accessible
5th rowNon accessible

Common Values

ValueCountFrequency (%)
Non accessible 1835
86.2%
Réservé PMR 44
 
2.1%
Accessible mais non réservé PMR 15
 
0.7%
Accessibilité inconnue 15
 
0.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:17.904760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:18.340153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
non 1850
47.9%
accessible 1850
47.9%
réservé 59
 
1.5%
pmr 59
 
1.5%
mais 15
 
0.4%
accessibilité 15
 
0.4%
inconnue 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s 3804
14.1%
e 3789
14.0%
c 3745
13.9%
1954
7.2%
i 1925
7.1%
n 1910
7.1%
b 1865
6.9%
o 1865
6.9%
l 1865
6.9%
a 1850
6.9%
Other values (11) 2397
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 3804
14.1%
e 3789
14.0%
c 3745
13.9%
1954
7.2%
i 1925
7.1%
n 1910
7.1%
b 1865
6.9%
o 1865
6.9%
l 1865
6.9%
a 1850
6.9%
Other values (11) 2397
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 3804
14.1%
e 3789
14.0%
c 3745
13.9%
1954
7.2%
i 1925
7.1%
n 1910
7.1%
b 1865
6.9%
o 1865
6.9%
l 1865
6.9%
a 1850
6.9%
Other values (11) 2397
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 3804
14.1%
e 3789
14.0%
c 3745
13.9%
1954
7.2%
i 1925
7.1%
n 1910
7.1%
b 1865
6.9%
o 1865
6.9%
l 1865
6.9%
a 1850
6.9%
Other values (11) 2397
8.9%

restriction_gabarit
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.2%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
aucune restriction
1858 
Hauteur maximale 1.90m
 
21
Véhicule electrique, hauteur 1.90 m
 
15
Hauteur maximale 1.85m
 
15

Length

Max length35
Median length18
Mean length18.20901
Min length18

Characters and Unicode

Total characters34761
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaucune restriction
2nd rowaucune restriction
3rd rowaucune restriction
4th rowaucune restriction
5th rowaucune restriction

Common Values

ValueCountFrequency (%)
aucune restriction 1858
87.3%
Hauteur maximale 1.90m 21
 
1.0%
Véhicule electrique, hauteur 1.90 m 15
 
0.7%
Hauteur maximale 1.85m 15
 
0.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:18.841491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:19.315196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
aucune 1858
47.7%
restriction 1858
47.7%
hauteur 51
 
1.3%
maximale 36
 
0.9%
1.90m 21
 
0.5%
véhicule 15
 
0.4%
electrique 15
 
0.4%
1.90 15
 
0.4%
m 15
 
0.4%
1.85m 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 3863
11.1%
u 3848
11.1%
t 3782
10.9%
i 3782
10.9%
r 3782
10.9%
c 3746
10.8%
n 3716
10.7%
1990
5.7%
a 1981
5.7%
s 1858
5.3%
Other values (16) 2413
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3863
11.1%
u 3848
11.1%
t 3782
10.9%
i 3782
10.9%
r 3782
10.9%
c 3746
10.8%
n 3716
10.7%
1990
5.7%
a 1981
5.7%
s 1858
5.3%
Other values (16) 2413
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3863
11.1%
u 3848
11.1%
t 3782
10.9%
i 3782
10.9%
r 3782
10.9%
c 3746
10.8%
n 3716
10.7%
1990
5.7%
a 1981
5.7%
s 1858
5.3%
Other values (16) 2413
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3863
11.1%
u 3848
11.1%
t 3782
10.9%
i 3782
10.9%
r 3782
10.9%
c 3746
10.8%
n 3716
10.7%
1990
5.7%
a 1981
5.7%
s 1858
5.3%
Other values (16) 2413
6.9%

station_deux_roues
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1824 
True
 
85
(Missing)
220 
ValueCountFrequency (%)
False 1824
85.7%
True 85
 
4.0%
(Missing) 220
 
10.3%
2024-05-18T12:36:19.723341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

puissance_nominale
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)0.3%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
7
1504 
22
154 
3
 
124
4
 
76
50
 
34

Length

Max length2
Median length1
Mean length1.1073861
Min length1

Characters and Unicode

Total characters2114
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row4
3rd row4
4th row22
5th row22

Common Values

ValueCountFrequency (%)
7 1504
70.6%
22 154
 
7.2%
3 124
 
5.8%
4 76
 
3.6%
50 34
 
1.6%
43 17
 
0.8%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:20.149539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:20.580951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
7 1504
78.8%
22 154
 
8.1%
3 124
 
6.5%
4 76
 
4.0%
50 34
 
1.8%
43 17
 
0.9%

Most occurring characters

ValueCountFrequency (%)
7 1504
71.1%
2 308
 
14.6%
3 141
 
6.7%
4 93
 
4.4%
5 34
 
1.6%
0 34
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2114
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 1504
71.1%
2 308
 
14.6%
3 141
 
6.7%
4 93
 
4.4%
5 34
 
1.6%
0 34
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2114
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 1504
71.1%
2 308
 
14.6%
3 141
 
6.7%
4 93
 
4.4%
5 34
 
1.6%
0 34
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2114
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 1504
71.1%
2 308
 
14.6%
3 141
 
6.7%
4 93
 
4.4%
5 34
 
1.6%
0 34
 
1.6%

prise_type_ef
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
True
1858 
False
 
51
(Missing)
220 
ValueCountFrequency (%)
True 1858
87.3%
False 51
 
2.4%
(Missing) 220
 
10.3%
2024-05-18T12:36:20.978053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

prise_type_2
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
True
1799 
False
 
110
(Missing)
220 
ValueCountFrequency (%)
True 1799
84.5%
False 110
 
5.2%
(Missing) 220
 
10.3%
2024-05-18T12:36:21.318603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

prise_type_combo_ccs
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1738 
True
 
171
(Missing)
220 
ValueCountFrequency (%)
False 1738
81.6%
True 171
 
8.0%
(Missing) 220
 
10.3%
2024-05-18T12:36:21.711645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

prise_type_chademo
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1738 
True
 
171
(Missing)
220 
ValueCountFrequency (%)
False 1738
81.6%
True 171
 
8.0%
(Missing) 220
 
10.3%
2024-05-18T12:36:22.203885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

prise_type_autre
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1909 
(Missing)
220 
ValueCountFrequency (%)
False 1909
89.7%
(Missing) 220
 
10.3%
2024-05-18T12:36:22.555043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

prise_type_3
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size4.3 KiB
False
1755 
True
 
154
(Missing)
220 
ValueCountFrequency (%)
False 1755
82.4%
True 154
 
7.2%
(Missing) 220
 
10.3%
2024-05-18T12:36:22.908858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

num_pdl
Text

MISSING 

Distinct372
Distinct (%)19.5%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
2024-05-18T12:36:23.765802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length31
Median length13
Mean length13.280775
Min length13

Characters and Unicode

Total characters25353
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row07416353009951
2nd row07190014394549
3rd row07398552727037
4th row07347756779654
5th row07431982531784
ValueCountFrequency (%)
comptage 15
 
0.8%
sous 15
 
0.8%
50023671243631 15
 
0.8%
50091256854973 15
 
0.8%
50042199442811 12
 
0.6%
50092850672853 9
 
0.5%
7379884132225 7
 
0.4%
7491316831349 7
 
0.4%
7480173560872 7
 
0.4%
7219392078082 7
 
0.4%
Other values (364) 1830
94.4%
2024-05-18T12:36:25.020751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3897
15.4%
4 2589
10.2%
2 2574
10.2%
5 2514
9.9%
3 2448
9.7%
0 2365
9.3%
6 2271
9.0%
1 2235
8.8%
9 2185
8.6%
8 2020
8.0%
Other values (13) 255
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 3897
15.4%
4 2589
10.2%
2 2574
10.2%
5 2514
9.9%
3 2448
9.7%
0 2365
9.3%
6 2271
9.0%
1 2235
8.8%
9 2185
8.6%
8 2020
8.0%
Other values (13) 255
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 3897
15.4%
4 2589
10.2%
2 2574
10.2%
5 2514
9.9%
3 2448
9.7%
0 2365
9.3%
6 2271
9.0%
1 2235
8.8%
9 2185
8.6%
8 2020
8.0%
Other values (13) 255
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 3897
15.4%
4 2589
10.2%
2 2574
10.2%
5 2514
9.9%
3 2448
9.7%
0 2365
9.3%
6 2271
9.0%
1 2235
8.8%
9 2185
8.6%
8 2020
8.0%
Other values (13) 255
 
1.0%

horaires
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
24/7
1909 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7636
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24/7
2nd row24/7
3rd row24/7
4th row24/7
5th row24/7

Common Values

ValueCountFrequency (%)
24/7 1909
89.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:25.524512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:25.889599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
24/7 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1909
25.0%
4 1909
25.0%
/ 1909
25.0%
7 1909
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1909
25.0%
4 1909
25.0%
/ 1909
25.0%
7 1909
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1909
25.0%
4 1909
25.0%
/ 1909
25.0%
7 1909
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1909
25.0%
4 1909
25.0%
/ 1909
25.0%
7 1909
25.0%

raccordement
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing220
Missing (%)10.3%
Memory size16.8 KiB
Direct
1894 
direct
 
15

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters11454
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowDirect
5th rowDirect

Common Values

ValueCountFrequency (%)
Direct 1894
89.0%
direct 15
 
0.7%
(Missing) 220
 
10.3%

Length

2024-05-18T12:36:26.251356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:36:26.582335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
direct 1909
100.0%

Most occurring characters

ValueCountFrequency (%)
i 1909
16.7%
r 1909
16.7%
e 1909
16.7%
t 1909
16.7%
c 1909
16.7%
D 1894
16.5%
d 15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1909
16.7%
r 1909
16.7%
e 1909
16.7%
t 1909
16.7%
c 1909
16.7%
D 1894
16.5%
d 15
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1909
16.7%
r 1909
16.7%
e 1909
16.7%
t 1909
16.7%
c 1909
16.7%
D 1894
16.5%
d 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1909
16.7%
r 1909
16.7%
e 1909
16.7%
t 1909
16.7%
c 1909
16.7%
D 1894
16.5%
d 15
 
0.1%

Correlations

2024-05-18T12:36:26.963390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
accessibilite_pmrarrondissementcode_insee_communedate_majid_pdcimplantation_stationlast_updatednbre_pdcpaiement_cbprise_type_2prise_type_3prise_type_chademoprise_type_combo_ccsprise_type_efpuissance_nominaleraccordementrestriction_gabaritstation_deux_rouesstatut_pdcurl_description_pdc
accessibilite_pmr1.0000.2350.1590.8140.0000.7640.0000.5920.0530.3280.0440.1060.1060.7640.4410.9990.8180.0181.0000.000
arrondissement0.2351.0000.9910.2040.9950.2871.0000.2200.0860.1190.0350.0000.0000.2870.1190.3380.3090.0420.3870.995
code_insee_commune0.1590.9911.0000.2190.9950.4571.0000.2810.0980.1520.0360.0000.0000.4570.1991.0000.4390.0850.3840.995
date_maj0.8140.2040.2191.0000.0000.7590.0000.3910.1020.3260.0720.1210.1210.7590.3390.9970.8130.0281.0000.000
id_pdc0.0000.9950.9950.0001.0000.0000.9950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9811.000
implantation_station0.7640.2870.4570.7590.0001.0000.0000.9980.0290.4260.0370.1340.1340.9900.9990.5180.9990.0161.0000.000
last_updated0.0001.0001.0000.0000.9950.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9860.995
nbre_pdc0.5920.2200.2810.3910.0000.9980.0001.0000.9650.6200.7750.7510.7510.9980.6400.7020.8150.2491.0000.000
paiement_cb0.0530.0860.0980.1020.0000.0290.0000.9651.0000.4410.7930.7510.7510.0290.9940.0070.0330.0731.0000.000
prise_type_20.3280.1190.1520.3260.0000.4260.0000.6200.4411.0000.0650.0470.0470.4260.9990.2190.4310.0421.0000.000
prise_type_30.0440.0350.0360.0720.0000.0370.0000.7750.7930.0651.0000.9410.9410.0370.9990.0000.0290.0551.0000.000
prise_type_chademo0.1060.0000.0000.1210.0000.1340.0000.7510.7510.0470.9411.0000.8880.1340.9710.0610.1360.0591.0000.000
prise_type_combo_ccs0.1060.0000.0000.1210.0000.1340.0000.7510.7510.0470.9410.8881.0000.1340.9710.0610.1360.0591.0000.000
prise_type_ef0.7640.2870.4570.7590.0000.9900.0000.9980.0290.4260.0370.1340.1341.0000.9990.5180.9990.0161.0000.000
puissance_nominale0.4410.1190.1990.3390.0000.9990.0000.6400.9940.9990.9990.9710.9710.9991.0000.5350.5760.7971.0000.000
raccordement0.9990.3381.0000.9970.0000.5180.0000.7020.0070.2190.0000.0610.0610.5180.5351.0000.9990.0001.0000.000
restriction_gabarit0.8180.3090.4390.8130.0000.9990.0000.8150.0330.4310.0290.1360.1360.9990.5760.9991.0000.0001.0000.000
station_deux_roues0.0180.0420.0850.0280.0000.0160.0000.2490.0730.0420.0550.0590.0590.0160.7970.0000.0001.0001.0000.000
statut_pdc1.0000.3870.3841.0000.9811.0000.9861.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.981
url_description_pdc0.0000.9950.9950.0001.0000.0000.9950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9811.000

Missing values

2024-05-18T12:35:36.568398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:35:38.880766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-18T12:35:41.463529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_idid_pdcstatut_pdcurl_description_pdclast_updatedcoordonneesxyadresse_stationcode_insee_communearrondissementid_pdc_localid_station_localid_station_itinerancenom_stationimplantation_stationnbre_pdcdate_majcondition_accesgratuitpaiement_actepaiement_cbpaiement_autrereservationobservationssiren_amenageurcontact_amenageurnom_amenageurnom_operateurcontact_operateurtelephone_operateurnom_enseignetarificationid_pdc_itinerancedate_mise_en_serviceaccessibilite_pmrrestriction_gabaritstation_deux_rouespuissance_nominaleprise_type_efprise_type_2prise_type_combo_ccsprise_type_chademoprise_type_autreprise_type_3num_pdlhorairesraccordement
066465a52dd6bc39c039ec745FR*V75*EPX11*05*4Disponiblehttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX11*05*42022-06-13T02:30:01+00:00{'lon': 2.3664377, 'lat': 48.865765}8 Boulevard Voltaire 75011 Paris7511111e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
166465a52dd6bc39c039ec746FR*V75*EPX20*09*2Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*22022-09-23T02:30:02+00:00{'lon': 2.4092, 'lat': 48.8731}6 Rue Guébriant 75020 Paris7512020e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
266465a52dd6bc39c039ec747FR*V75*EPX20*09*3Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*32022-09-23T02:30:02+00:00{'lon': 2.4092, 'lat': 48.8731}6 Rue Guébriant 75020 Paris7512020e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
366465a52dd6bc39c039ec748FR*V75*E9005*02*1Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*E9005*02*12022-11-22T03:20:17+00:00{'lon': 2.35242, 'lat': 48.84477}55 Rue Monge 75005 Paris7510505e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
466465a52dd6bc39c039ec749FR*V75*EPX12*04*4Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX12*04*42023-05-24T02:30:05+00:00{'lon': 2.3922555, 'lat': 48.83641}251 Rue de Charenton 75012 Paris7511212e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
566465a52dd6bc39c039ec74aFR*V75*EPX13*05*1Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX13*05*12023-06-06T02:30:04+00:00{'lon': 2.356917, 'lat': 48.83039}188 Avenue de Choisy 75013 Paris7511313e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
666465a52dd6bc39c039ec74bFR*V75*EPX18*11*5Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX18*11*52023-10-19T02:30:06+00:00{'lon': 2.3375535, 'lat': 48.88508}1bis Rue Ravignan 75018 Paris7511818e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
766465a52dd6bc39c039ec74cFR*V75*EPX15*13*5Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX15*13*52024-01-03T03:30:06+00:00{'lon': 2.312675, 'lat': 48.838547}29 Rue Cotentin 75015 Paris7511515e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
866465a52dd6bc39c039ec74dFR*V75*EPX15*13*6Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX15*13*62024-01-03T03:30:06+00:00{'lon': 2.312675, 'lat': 48.838547}29 Rue Cotentin 75015 Paris7511515e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
966465a52dd6bc39c039ec74eFR*V75*EPX19*17*5Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX19*17*52024-02-26T03:30:05+00:00{'lon': 2.370891, 'lat': 48.882175}4 Avenue Secrétan 75019 Paris7511919e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
_idid_pdcstatut_pdcurl_description_pdclast_updatedcoordonneesxyadresse_stationcode_insee_communearrondissementid_pdc_localid_station_localid_station_itinerancenom_stationimplantation_stationnbre_pdcdate_majcondition_accesgratuitpaiement_actepaiement_cbpaiement_autrereservationobservationssiren_amenageurcontact_amenageurnom_amenageurnom_operateurcontact_operateurtelephone_operateurnom_enseignetarificationid_pdc_itinerancedate_mise_en_serviceaccessibilite_pmrrestriction_gabaritstation_deux_rouespuissance_nominaleprise_type_efprise_type_2prise_type_combo_ccsprise_type_chademoprise_type_autreprise_type_3num_pdlhorairesraccordement
2119664870e5c7ace53b266c3136NaNEn serviceNaNNaN{'lon': 2.272306, 'lat': 48.847847}8 Rue Rémusat 75016 Paris7511616e ArrondissementFR*V75*EPX16*29*3FR*V75*PPX16*29FRV75PPX1629Paris | Rue Rémusat 8Voirie62023-04-03Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EPX162932021-10-19Non accessibleaucune restrictionFalse7TrueTrueFalseFalseFalseFalse746628065191624/7Direct
2120664870e5c7ace53b266c3137NaNEn serviceNaNNaN{'lon': 2.2849722, 'lat': 48.885426}28 Avenue de la Porte de Villiers 75017 Paris7511717e ArrondissementFR*V75*EPX17*23*6FR*V75*PPX17*23FRV75PPX1723Paris | Avenue de la Porte de Villiers 28Voirie62023-04-03Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EPX172362021-12-01Non accessibleaucune restrictionFalse7TrueTrueFalseFalseFalseFalse754298108943824/7Direct
2121664870e5c7ace53b266c3138NaNEn serviceNaNNaN{'lon': 2.2922037, 'lat': 48.882423}170 boulevard Pereire 75017 Paris7511717e ArrondissementFR*V75*EPX17*25*4FR*V75*PPX17*25FRV75PPX1725Paris | boulevard Pereire 170Voirie62023-04-03Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EPX172542021-11-23Non accessibleaucune restrictionFalse7TrueTrueFalseFalseFalseFalse757930525549724/7Direct
2122664870e5c7ace53b266c3139NaNEn serviceNaNNaN{'lon': 2.344978, 'lat': 48.891644}62 Rue Ramey 75018 Paris7511818e ArrondissementFR*V75*EPX18*13*4FR*V75*PPX18*13FRV75PPX1813Paris | Rue Ramey 62Voirie62023-04-03Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EPX181342021-12-01Non accessibleaucune restrictionFalse7TrueTrueFalseFalseFalseFalse758176545980524/7Direct
2123664870e5c7ace53b266c313aNaNEn serviceNaNNaN{'lon': 2.3305461, 'lat': 48.884415}1 Avenue Rachel 75018 Paris7511818e ArrondissementFR*V75*EPX18*14*1FR*V75*PPX18*14FRV75PPX1814Paris | Avenue Rachel 1Voirie52023-04-03Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EPX181412021-12-01Non accessibleaucune restrictionFalse7TrueTrueFalseFalseFalseFalse755296661744824/7Direct
2124664870e5c7ace53b266c313bNaNEn serviceNaNNaN{'lon': 2.3864658, 'lat': 48.87387}356 Rue des Pyrénées 75020 Paris7512020e ArrondissementFR*V75*EPX20*15*4FR*V75*PPX20*15FRV75PPX2015Paris | Rue des Pyrénées 356Voirie62023-04-03Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EPX201542021-11-16Non accessibleaucune restrictionFalse7TrueTrueFalseFalseFalseFalse724327051529624/7Direct
2125664870e5c7ace53b266c313cNaNEn serviceNaNNaN{'lon': 2.2558389, 'lat': 48.846973}1-3 Av. du Général Sarrail 75016 Paris7511616e ArrondissementFR*V75*EHBSAE*PDA*04*3FR*V75*PHBSAE*PDAFRV75PHBSAEPDAParis | SAEMES Parking Porte d'AuteuilParking public152023-11-15Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EHBSAEPDA0432022-10-19Accessibilité inconnueHauteur maximale 1.85mFalse50FalseFalseFalseTrueFalseFalse5009125685497324/7Direct
2126664870e5c7ace53b266c313dNaNEn serviceNaNNaN{'lon': 2.2558389, 'lat': 48.846973}1-3 Av. du Général Sarrail 75016 Paris7511616e ArrondissementFR*V75*EHBSAE*PDA*02*2FR*V75*PHBSAE*PDAFRV75PHBSAEPDAParis | SAEMES Parking Porte d'AuteuilParking public152023-11-15Accès librefalsetruefalsetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EHBSAEPDA0222023-12-28Accessibilité inconnueHauteur maximale 1.85mFalse50FalseFalseTrueFalseFalseFalse5009125685497324/7Direct
2127664870e5c7ace53b266c313eNaNEn serviceNaNNaN{'lon': 2.2558389, 'lat': 48.846973}1-3 Av. du Général Sarrail 75016 Paris7511616e ArrondissementFR*V75*EHBSAE*PDA*05*1FR*V75*PHBSAE*PDAFRV75PHBSAEPDAParis | SAEMES Parking Porte d'AuteuilParking public152023-11-15Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EHBSAEPDA0512022-10-19Accessibilité inconnueHauteur maximale 1.85mFalse43FalseTrueFalseFalseFalseFalse5009125685497324/7Direct
2128664870e5c7ace53b266c313fNaNEn serviceNaNNaN{'lon': 2.2558389, 'lat': 48.846973}1-3 Av. du Général Sarrail 75016 Paris7511616e ArrondissementFR*V75*EHBSAE*PDA*03*3FR*V75*PHBSAE*PDAFRV75PHBSAEPDAParis | SAEMES Parking Porte d'AuteuilParking public152023-11-15Accès librefalsetruetruetruefalse[Paiement autre - badge RFID Belib ou autre, application Belib, QRCode sur chaque PDC, site internet Belib, Plus d'information - https://belib.paris.fr]531680445assistance.technique@tevgo.frTOTALENERGIESTOTALENERGIESassistance.technique@tevgo.fr01 85 16 94 02Belib'https://belib.parisFRV75EHBSAEPDA0332022-10-19Accessibilité inconnueHauteur maximale 1.85mFalse50FalseFalseFalseTrueFalseFalse5009125685497324/7Direct

Duplicate rows

Most frequently occurring

id_pdcstatut_pdcurl_description_pdclast_updatedadresse_stationcode_insee_communearrondissementid_pdc_localid_station_localid_station_itinerancenom_stationimplantation_stationnbre_pdcdate_majcondition_accesgratuitpaiement_actepaiement_cbpaiement_autrereservationsiren_amenageurcontact_amenageurnom_amenageurnom_operateurcontact_operateurtelephone_operateurnom_enseignetarificationid_pdc_itinerancedate_mise_en_serviceaccessibilite_pmrrestriction_gabaritstation_deux_rouespuissance_nominaleprise_type_efprise_type_2prise_type_combo_ccsprise_type_chademoprise_type_autreprise_type_3num_pdlhorairesraccordement# duplicates
0FR*V75*E9005*02*1Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*E9005*02*12022-11-22T03:20:17+00:0055 Rue Monge 75005 Paris7510505e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
1FR*V75*EPX11*05*4Disponiblehttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX11*05*42022-06-13T02:30:01+00:008 Boulevard Voltaire 75011 Paris7511111e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
2FR*V75*EPX12*04*4Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX12*04*42023-05-24T02:30:05+00:00251 Rue de Charenton 75012 Paris7511212e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
3FR*V75*EPX13*05*1Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX13*05*12023-06-06T02:30:04+00:00188 Avenue de Choisy 75013 Paris7511313e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
4FR*V75*EPX15*13*5Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX15*13*52024-01-03T03:30:06+00:0029 Rue Cotentin 75015 Paris7511515e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
5FR*V75*EPX15*13*6Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX15*13*62024-01-03T03:30:06+00:0029 Rue Cotentin 75015 Paris7511515e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
6FR*V75*EPX18*11*5Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX18*11*52023-10-19T02:30:06+00:001bis Rue Ravignan 75018 Paris7511818e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
7FR*V75*EPX19*17*5Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX19*17*52024-02-26T03:30:05+00:004 Avenue Secrétan 75019 Paris7511919e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
8FR*V75*EPX20*09*2Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*22022-09-23T02:30:02+00:006 Rue Guébriant 75020 Paris7512020e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22
9FR*V75*EPX20*09*3Inconnuhttps://parisdata.opendatasoft.com/explore/dataset/belib-points-de-recharge-pour-vehicules-electriques-donnees-statiques/table/?disjunctive.nbre_pdc&disjunctive.accessibilite&disjunctive.horaires_sav&disjunctive.postal_code&disjunctive.acces_recharge&disjunctive.type_prise&disjunctive.puiss_max&q=FR*V75*EPX20*09*32022-09-23T02:30:02+00:006 Rue Guébriant 75020 Paris7512020e ArrondissementNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22